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AWS · Security · Automation

Cloud Security
Automation

January 2025 – July 2025

Project Overview

Built an automated security posture pipeline to detect configuration drift across unmanaged infrastructure and surface risk in real time via a centralized Splunk dashboard. The system combined AWS infrastructure scanning, Python/Bash automation, and GRC framework alignment to move from reactive, manual security reviews to continuous, automated visibility.

Project Timeline

01
Risk Assessment & Scoping
January 2025
Identified unmanaged infrastructure assets, catalogued risk exposure categories, and defined GRC framework requirements for the posture pipeline.
02
Pipeline Architecture Design
February 2025
Designed the detection pipeline: AWS resource enumeration → configuration state capture → drift comparison → alert routing.
03
Drift Detection Automation
March – April 2025
Implemented Python and Bash scripts to continuously compare live AWS configuration state against approved security baselines and flag deviations.
04
Dashboard Integration
May 2025
Integrated pipeline output into Splunk dashboards for real-time risk visibility, alert aggregation, and trend analysis.
05
Documentation & Remediation Playbooks
June – July 2025
Authored remediation runbooks for each detected drift category, enabling operations teams to resolve findings without security team escalation.

Tech Stack

☁️
AWS
🐍
Python
💻
Bash
🔍
Splunk
🔄
CI/CD
🛡️
GRC Frameworks
🤖
Security Automation

Problem Statement

Unmanaged servers across the infrastructure were drifting from approved security configurations with no automated mechanism to detect or alert on those changes. The lack of centralized visibility meant that configuration deviations — open ports, weakened IAM policies, disabled logging — could persist undetected, creating an expanding unauthorized access risk surface with no clear remediation trigger.

Approach & Solution

1

Posture-Mapping Pipelines

Built AWS enumeration scripts that captured the live configuration state of all in-scope resources and compared against defined security baselines.

2

Automated Drift Detection

Python and Bash automation continuously ran posture checks on a scheduled cadence, generating structured findings for any configuration that deviated from baseline.

3

Real-Time Splunk Dashboard

Forwarded all findings to Splunk where a purpose-built dashboard provided real-time risk visibility, alert severity classification, and trend tracking.

4

Remediation Playbooks

Authored step-by-step remediation playbooks for each finding category, enabling self-service resolution and reducing mean time to remediate.

Key Outcomes

👁️
Real-Time
Security Visibility Across All Monitored Assets
📖
Standard
Remediation Workflows Documented & Deployed
⚡
Scalable
Automation Framework Extensible to New Assets

Lessons Learned

  • Automation without documentation creates knowledge silos — the pipeline only delivered lasting value when paired with runbooks that let any team member act on its findings independently.
  • Dashboard visibility is the bridge between technical findings and business action; raw alert data without visualization fails to drive prioritization at the management level.
  • GRC alignment from the start — not retrofitted at the end — is what turns a technical detection tool into a compliance-grade security control.
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